# LICENSE HEADER MANAGED BY add-license-header
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# Copyright 2018 Kornia Team
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
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#     http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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from typing import Dict, Optional, Tuple, Union

import torch
from torch.distributions import Bernoulli

from kornia.augmentation.random_generator.base import RandomGeneratorBase, UniformDistribution
from kornia.augmentation.utils import _adapted_rsampling, _adapted_sampling, _common_param_check, _joint_range_check
from kornia.core.utils import _extract_device_dtype

__all__ = ["MixupGenerator"]


class MixupGenerator(RandomGeneratorBase):
    r"""Generate mixup indexes and lambdas for a batch of inputs.

    Args:
        lambda_val (torch.Tensor, optional): min-max strength for mixup images, ranged from [0., 1.].
            If None, it will be set to tensor([0., 1.]), which means no restrictions.

    Returns:
        A dict of parameters to be passed for transformation.
            - mix_pairs (torch.Tensor): element-wise probabilities with a shape of (B,).
            - mixup_lambdas (torch.Tensor): element-wise probabilities with a shape of (B,).

    Note:
        The generated random numbers are not reproducible across different devices and dtypes. By default,
        the parameters will be generated on CPU in float32. This can be changed by calling
        ``self.set_rng_device_and_dtype(device="cuda", dtype=torch.float64)``.

    """

    def __init__(self, lambda_val: Optional[Union[torch.Tensor, Tuple[float, float]]] = None, p: float = 1.0) -> None:
        super().__init__()
        self.lambda_val = lambda_val
        self.p = p

    def __repr__(self) -> str:
        repr = f"lambda_val={self.lambda_val}"
        return repr

    def make_samplers(self, device: torch.device, dtype: torch.dtype) -> None:
        if self.lambda_val is None:
            lambda_val = torch.tensor([0.0, 1.0], device=device, dtype=dtype)
        else:
            lambda_val = torch.as_tensor(self.lambda_val, device=device, dtype=dtype)

        _joint_range_check(lambda_val, "lambda_val", bounds=(0, 1))
        self.lambda_sampler = UniformDistribution(lambda_val[0], lambda_val[1], validate_args=False)
        self.prob_sampler = Bernoulli(torch.tensor(float(self.p), device=device, dtype=dtype))

    def forward(self, batch_shape: Tuple[int, ...], same_on_batch: bool = False) -> Dict[str, torch.Tensor]:
        batch_size = batch_shape[0]

        _common_param_check(batch_size, same_on_batch)
        _device, _dtype = _extract_device_dtype([self.lambda_val])

        with torch.no_grad():
            batch_probs: torch.Tensor = _adapted_sampling((batch_size,), self.prob_sampler, same_on_batch)
        mixup_pairs: torch.Tensor = torch.randperm(batch_size, device=_device, dtype=_dtype).long()
        mixup_lambdas: torch.Tensor = _adapted_rsampling((batch_size,), self.lambda_sampler, same_on_batch)
        mixup_lambdas = mixup_lambdas * batch_probs

        return {
            "mixup_pairs": mixup_pairs.to(device=_device, dtype=torch.long),
            "mixup_lambdas": mixup_lambdas.to(device=_device, dtype=_dtype),
        }
